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This paper presents a novel approach to fall prediction for bipedal robots,specifically targeting the detection of potential falls while standing causedby abrupt, incipient, and intermittent faults. Leveraging a 1D convolutionalneural network (CNN), our method aims to maximize lead time for fall predictionwhile minimizing false positive rates. The proposed algorithm uniquelyintegrates the detection of various fault types and estimates the lead time forpotential falls. Our contributions include the development of an algorithmcapable of detecting abrupt, incipient, and intermittent faults in full-sizedrobots, its implementation using both simulation and hardware data for ahumanoid robot, and a method for estimating lead time. Evaluation metrics,including false positive rate, lead time, and response time, demonstrate theefficacy of our approach. Particularly, our model achieves impressive leadtimes and response times across different fault scenarios with a false positiverate of 0. The findings of this study hold significant implications forenhancing the safety and reliability of bipedal robotic systems.

Fall Prediction for Bipedal Robots: The Standing Phase